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Spatial Evolution of the US Urban System Author info | Abstract | Publisher info | Download info | Related research | Statistics Yannis M. Ioannides ()
Henry G. Overman ()
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We test implications of econom c geography models for location,size and growth of cities with US Census data for 1900 - 1990. Our tests involve non-parametr c estimations of stochastic kernels for the distributions of city sizes and growth rates, conditional on various measures of market potential and on features of neighbors. We show that while these relationships change during the twentieth century, by 1990 they stabilize such that the size distribution of cities conditional on a range of spatial variables are all roughly independent of these conditioning variables. In contrast, similar results suggest that there is a spatial element to the city wage distribution. Our parametric estimations for growth rates against market potential, entry of neighbors, and own lagged population imply a negative effect of market potential on growth rates, unless own lagged population is also ncluded, in which case market potential has a positive effect and own lagged population a negative one. Cities grow faster when they are small relative to their market potential. In total, our results support some theoretical predictions, but also provide a number of interesting puzzles.
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Paper provided by Department of Economics, Tufts University in its series Discussion Papers Series, Department of Economics, Tufts University with number
0018.
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Date of creation: 2000Date of revision:
Handle: RePEc:tuf:tuftec:0018Contact details of provider: Postal: Medford, MA 02155, USA Phone: (617) 627-3560 Fax: (617) 627-3917 Web page: http://ase.tufts.edu/econ
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Keywords: urban growth ; spatial evolution ; economics geography ; Other versions of this item:
Find related papers by JEL classification: R00 - Urban, Rural, and Regional Economics - - General - - - General C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
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